Wireless Sensor Networks (WSNs) consisted of various low-cost devices that had limited battery power for surveillance of certain vicinity. The objective of WSNs had accomplished the given tasks within time in an efficient manner. The main concern was to prolong the network lifetime to save energy. The heterogeneous nodes deployed in the given vicinity divided into two INSTANT-OFF and NEVER-OFF states and then each one further subdivided into three (Good, Better and Best) by Fuzzy Inference System (FIS). The cluster boundary was defined by parameter Distance from Base Station (DisBS) and its linguistic terms such as very close, close, medium, far and very far to meet the unequal clustering approach. The INSTANT-OFF (Good, Better and Best) which had three states active, idle, sleep and always worked as Cluster Members (CMs) to sense the physical environment. The NEVER-OFF (Good, Better and Best) had active and idle states. The first two most optimum NEVER-OFF selected as Cluster Head (CH) and Data Collector (DC) and the remaining belonged to CMs. If only one NEVER-OFF (Good, Better or Best) was available in a cluster then both jobs as CH as well as DC performed by it. If none NEVER-OFF (Good, Better and Best) were available in the cluster then re-clustering setup took place by BS. In this research, Near-Optimal Energy Aware Approach through INSTAN-OFF and NEVER-OFF Clustering by Fuzzy Logic (NOEA-INCFL), the energy was consumed during sensing, processing and transmission phase by its appropriate nodes. The CMs worked as a reactive manner and saved energy by idle and sleep states while the CH and DC worked in a proactive mode and saved energy in idle state. The sensing job was done by CMs that consumed a minor amount of energy and transmitted packets of 200 bits length to DC. The second more energy consumption job was processing, performed by DC. The DC received packets of 200 bits length from CMs and aggregated them into 6400 bits length packets then delivered it to CH. The most energy consumption job was communication with BS that performed by CH hop by hop through other CH. The unequal clustering approach maintained the consumption of energy levels throughout WSNs. The reactive and proactive mechanisms saved the energy as 85.1033% in 2000 rounds; increased lifetime up to 774 rounds, re-clustering setup took place after 1912 rounds as well as enhanced the throughput as 100% and latency time 0.001123 in the first round. This approach was implemented and evaluated through MATLABand its simulation in OMNET++.